AIforScience大时代，撬动科学研发万亿赛道

Investment Rating - The industry investment rating is "Increase" [5] Core Insights - The era of AI for Science (AI4S) is transforming scientific research, particularly in materials development, which has become increasingly complex due to multi-objective optimization requirements. AI4S utilizes AI algorithms to enhance molecular structure insights through quantum physics calculations and integrates real-world data from high-throughput robotic laboratories, significantly shortening research cycles [1] - The potential market size for AI4S in the pharmaceutical sector is estimated at approximately $108.2 billion, based on a 33% value share of the preclinical research market within the global pharmaceutical market of $1.64 trillion. Additionally, assuming a 25% penetration rate in sectors such as chemicals, pharmaceuticals, new energy, alloys, displays, and semiconductors, the total AI4S market demand could reach around $148.6 billion [2] - Key application areas for AI4S include innovative drug development, where the complexity of drug research aligns well with AI capabilities, and space photovoltaics, particularly with perovskite materials that can significantly enhance satellite energy efficiency [3] Summary by Sections AI4S Empowerment in Scientific Research - AI4S capabilities encompass "reading, computing, and doing." For instance, the company Tai Holdings has developed a patent data mining platform that can extract literature and patent data in one hour with a 95% accuracy rate, and over 200 AI models that enhance research speed and precision [1] Market Size and Potential - The pharmaceutical sector's AI4S market potential is approximately $108.2 billion, while the overall market demand across six sectors could reach about $148.6 billion under a 25% penetration assumption [2] Notable Application Areas - Innovative drug development is a primary focus for AI4S due to the high investment and complexity involved. Additionally, perovskite materials in space photovoltaics present a promising area for AI optimization, addressing technical challenges related to stability and efficiency [3][4]